Machine-Learning-Assisted Procoagulant Extracellular Vesicle Barcode Assay toward High-Performance Evaluation of Thrombosis-Induced Death Risk in Cancer Patients
Quick Facts
- Publication title: Machine-Learning-Assisted Procoagulant Extracellular Vesicle Barcode Assay toward High-Performance Evaluation of Thrombosis-Induced Death Risk in Cancer Patients
- Journal: ACS Nano
- Year: 2023
- DOI: 10.1021/acsnano.3c04615
- PMID/PMCID: 37791763
Research overview
From ordinary plasma samples, the PEVB assay can evaluate potential VTE risk by integrating TiNFs-based EV capture and in situ EV procoagulant ability test with machine-learning-assisted clinical data analysis. Unfortunately, the frequent misdiagnosis of VTE owing to the lack of accurate and efficient evaluation approaches may cause belated medical intervention and even sudden death.
Key findings
Venous thromboembolism (VTE) is the most fatal complication in cancer patients.
Unfortunately, the frequent misdiagnosis of VTE owing to the lack of accurate and efficient evaluation approaches may cause belated medical intervention and even sudden death.
Herein, we present a rapid, easily operable, highly specific, and highly sensitive procoagulant extracellular vesicle barcode (PEVB) assay composed of TiO2 nanoflower (TiNFs) for visually evaluating VTE risk in cancer patients.
TiNFs demonstrate rapid label-free EV capture capability by the synergetic effect of TiO2-phospholipids molecular interactions and topological interactions between TiNFs and EVs.
Echo Biotech Role
Echo Biotech contributed EV isolation and purification, EV tracing or fluorescent labeling; the study also used or cited Exosupur®.
Related platforms: Exoomics®, Research Reagents & Tools
Related services and capabilities: Biofluid EV Isolation & Purification, EV Tracing & Uptake Analysis, Research Reagent / Product Supply
Related products or reagents: Exosupur® EV Isolation/Purification Kit
References
Original publication: Machine-Learning-Assisted Procoagulant Extracellular Vesicle Barcode Assay toward High-Performance Evaluation of Thrombosis-Induced Death Risk in Cancer Patients ACS Nano. 2023. DOI: 10.1021/acsnano.3c04615. PMID/PMCID: 37791763.